Image quality performance assessment (QA) and performance control (QC) for digital computed radiography systems (computed radiography CR or direct radiography DR) are of crucial importance within the context of medical diagnostic imaging. QA/QC testing and reporting of the results for digital X-ray projection image acquisition systems has globally evolved from a moral obligation status towards mandatory requirements, imposed by local health care regulations over the last decade.
Quality control can be performed at several instances during the life cycle of a digital radiography system. Manufacturers of digital computed radiography equipment can integrate image quality performance testing as part of their final QC-testing procedures, performed prior to customer shipment. Also, hospitals can perform acceptance testing. This acceptance testing relies on the results from image quality performance testing, executed after initial delivery, move, reconfiguration or repair of the image acquisition system or its vital components. Furthermore, periodic quality control testing, also referred to as constancy testing, can be part of a quality assurance program which tracks the image quality performance of computed radiography systems by reporting their time-consecutive QC-results, collected on a regular basis (daily, weekly, monthly, . . . ) to survey the system performance status relative to the image quality requirements and also to gather input for preventive maintenance scheduling.
As shown in FIG. 1 an image acquisition system for computed radiography is composed of various, linked subcomponents e.g., a console, a generator, an X-ray source, a dose monitor (optional) and a detector/digitizer.
The X-ray source is driven by the generator, receiving commands, settings and synchronization from the console. The generator settings, the tube assembly and the external filters, positioned in the beam-path near the X-ray tube, determine the energy spectrum of the generated photons used for projection imaging. An optional dose monitor inside the beam-path can provide accurate exposure information. An absorption shadow of an object (quality control target, patient), present in the optical path during exposure, is projected onto a X-ray sensitive detection surface, external to (storage phosphor medium based for CR) or integrated inside (solid state sensor based for DR) a digitizer. The digitizer converts the object's impinging X-ray shadow, captured and stored by the detector, into a digital image. Additional information, related to the image captured, such as: time, location, system configuration, system settings, imaging mode, exposure conditions, spectrum, dose, . . . , which can be relevant for routing, processing and storage of the generated image can be attached to the image data file. The obtained raw images, if used for medical purposes, are subject to dedicated diagnostic image processing to make them optimally suited for soft- or hardcopy inspection by radiologists or for computer aided detection purposes. The processed images can be visualized, archived, communicated, printed etc. on e.g., PACS systems.
The image quality performance testing of the image acquisition system, the front-end of the projection radiography imaging chain, performed during acceptance testing or constancy testing does not require X-ray exposure of human or animal beings.
Image quality performance testing involves acquisition and processing of digital images according to predetermined, well defined procedures and X-ray exposure conditions (sequence, timing, geometry, spectrum, dose, . . . ) by projection imaging one or multiple, dedicated quality control targets, also referred to as phantom objects, positioned in the beam-path between the X-ray source and the detection surface. These QC-targets can be composed of various objects and materials, pattern-wise arranged and spatially distributed inside the target such that the target is optimally suited as a test-object to produce images under exposure conditions, representative for the medical use of the equipment.
The obtained image data and the related information, contained inside the QC-target image, can be processed by dedicated QC-analysis software according to specific algorithms. These algorithms are designed to discriminate and measure the various, characteristic image quality performance parameters, representing the imaging capabilities of the system under test, and relate the calculated performance status to the required image quality criteria, proposed or mandatory for medical use. The QC-test results and comparative findings can be automatically reported and these reports can be archived in a PACS system or in a dedicated QC-document database (repository).
Since image acquisition systems for computed radiography are composed of various linked sub-components, the end-resulting image quality performance of the overall system will be determined by the individual image quality performance contributions of the various sub-components, part of the projection imaging chain. Image sharpness for instance, a typical important image quality performance parameter often analyzed, not only depends on the digitizer's modulation transfer function but is also influenced by the selected X-ray tube focus-size and by spatial blurring in the detector-plane. This spatial blurring can occur due to X-ray scatter inside the detector as a function of detector composition and photon spectrum or by strayed stimulation-light during plate-readout (CR).
For this reason, overall image quality performance testing often breaks up into multiple, separate QC-tests to evaluate the proper operation of the various system components, each executed under well-controlled geometry and exposure conditions according to predetermined and well-defined test procedures.
Since the QC-target, a prerequisite to create QC-target images, is an integral part of the image acquisition system during QC-testing, it will, like the other system-components that are part of the imaging chain, have an impact on the properties of the projected target-shadow, of which the QC-target image is generated and of which the image quality performance parameters are derived by calculations.
Image quality performance acceptance criteria are established by QC-analysis of QC-target images, captured from a nominal reference QC-target for each typical, representative system configuration under well-controlled exposure conditions. During these tests to establish the reference acceptance criteria for a given image acquisition system only system components showing nominal performance should be part of the imaging chain. These image quality performance acceptance criteria found can be used to evaluate the performance status of medical diagnostic image acquisition systems at the end of the manufacturing chain and out in the field.
Unlike the other system components, the QC-target is never part of the imaging chain during normal operation of the digital radiography equipment. By consequence QC-target physical property variability should not have any impact on the QC-test results since these should only reflect the real image quality, representative for system performance in normal, clinical use mode.
To ensure that these real QC-test results, for a given system at a given point in time, are independent of the QC-target having a specific serial number used, each QC-target manufactured should be a perfect duplicate of the nominal reference QC-target, used to determine the image quality performance acceptance criteria. However, due to tolerances and physical property variability of components used during QC-target assembly, there will always be an inevitable amount of uncertainty about the QC-test-results obtained if QC-target images are the basis for image quality performance evaluation of digital radiography systems.
To overcome the above mentioned problems a need exist to significantly reduce the impact of QC-target related tolerances and physical property variability on the performance results obtained during QA/QC-testing of digital radiography equipment.